{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 导入工具包\n",
    "import os\n",
    "import sys\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 读取数据文件并总体概览\n",
    "data_path = '/home/ai/study/week1/exercises/basic/'\n",
    "data_filename = 'day.csv'\n",
    "data_full_filename = data_path + data_filename\n",
    "\n",
    "if not os.path.exists(data_full_filename):\n",
    "    print '[-] file(%s) is not found!', data_full_filename\n",
    "    sys.exit(-1)\n",
    "    \n",
    "data = pd.read_csv(data_full_filename)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 数据基本信息"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(731, 16)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>instant</th>\n",
       "      <th>dteday</th>\n",
       "      <th>season</th>\n",
       "      <th>yr</th>\n",
       "      <th>mnth</th>\n",
       "      <th>holiday</th>\n",
       "      <th>weekday</th>\n",
       "      <th>workingday</th>\n",
       "      <th>weathersit</th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>hum</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>casual</th>\n",
       "      <th>registered</th>\n",
       "      <th>cnt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2011-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.344167</td>\n",
       "      <td>0.363625</td>\n",
       "      <td>0.805833</td>\n",
       "      <td>0.160446</td>\n",
       "      <td>331</td>\n",
       "      <td>654</td>\n",
       "      <td>985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2011-01-02</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.363478</td>\n",
       "      <td>0.353739</td>\n",
       "      <td>0.696087</td>\n",
       "      <td>0.248539</td>\n",
       "      <td>131</td>\n",
       "      <td>670</td>\n",
       "      <td>801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>2011-01-03</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.196364</td>\n",
       "      <td>0.189405</td>\n",
       "      <td>0.437273</td>\n",
       "      <td>0.248309</td>\n",
       "      <td>120</td>\n",
       "      <td>1229</td>\n",
       "      <td>1349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>2011-01-04</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.212122</td>\n",
       "      <td>0.590435</td>\n",
       "      <td>0.160296</td>\n",
       "      <td>108</td>\n",
       "      <td>1454</td>\n",
       "      <td>1562</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>2011-01-05</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.226957</td>\n",
       "      <td>0.229270</td>\n",
       "      <td>0.436957</td>\n",
       "      <td>0.186900</td>\n",
       "      <td>82</td>\n",
       "      <td>1518</td>\n",
       "      <td>1600</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   instant      dteday  season  yr  mnth  holiday  weekday  workingday  \\\n",
       "0        1  2011-01-01       1   0     1        0        6           0   \n",
       "1        2  2011-01-02       1   0     1        0        0           0   \n",
       "2        3  2011-01-03       1   0     1        0        1           1   \n",
       "3        4  2011-01-04       1   0     1        0        2           1   \n",
       "4        5  2011-01-05       1   0     1        0        3           1   \n",
       "\n",
       "   weathersit      temp     atemp       hum  windspeed  casual  registered  \\\n",
       "0           2  0.344167  0.363625  0.805833   0.160446     331         654   \n",
       "1           2  0.363478  0.353739  0.696087   0.248539     131         670   \n",
       "2           1  0.196364  0.189405  0.437273   0.248309     120        1229   \n",
       "3           1  0.200000  0.212122  0.590435   0.160296     108        1454   \n",
       "4           1  0.226957  0.229270  0.436957   0.186900      82        1518   \n",
       "\n",
       "    cnt  \n",
       "0   985  \n",
       "1   801  \n",
       "2  1349  \n",
       "3  1562  \n",
       "4  1600  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 作业不要求y列 ‘casual'和'registered'，先剔除\n",
    "# 执行下面代码，重新执行整个notebook，后读入数据已经没有这两列了.....\n",
    "try:\n",
    "  data = data.drop('casual', axis = 1)\n",
    "  data = data.drop('registered', axis = 1)\n",
    "except:\n",
    "  pass\n",
    "\n",
    "# 取2011年数据用作分析\n",
    "data = data[data.yr == 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(365, 14)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>instant</th>\n",
       "      <th>dteday</th>\n",
       "      <th>season</th>\n",
       "      <th>yr</th>\n",
       "      <th>mnth</th>\n",
       "      <th>holiday</th>\n",
       "      <th>weekday</th>\n",
       "      <th>workingday</th>\n",
       "      <th>weathersit</th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>hum</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>cnt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2011-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.344167</td>\n",
       "      <td>0.363625</td>\n",
       "      <td>0.805833</td>\n",
       "      <td>0.160446</td>\n",
       "      <td>985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2011-01-02</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.363478</td>\n",
       "      <td>0.353739</td>\n",
       "      <td>0.696087</td>\n",
       "      <td>0.248539</td>\n",
       "      <td>801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>2011-01-03</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.196364</td>\n",
       "      <td>0.189405</td>\n",
       "      <td>0.437273</td>\n",
       "      <td>0.248309</td>\n",
       "      <td>1349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>2011-01-04</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.212122</td>\n",
       "      <td>0.590435</td>\n",
       "      <td>0.160296</td>\n",
       "      <td>1562</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>2011-01-05</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.226957</td>\n",
       "      <td>0.229270</td>\n",
       "      <td>0.436957</td>\n",
       "      <td>0.186900</td>\n",
       "      <td>1600</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   instant      dteday  season  yr  mnth  holiday  weekday  workingday  \\\n",
       "0        1  2011-01-01       1   0     1        0        6           0   \n",
       "1        2  2011-01-02       1   0     1        0        0           0   \n",
       "2        3  2011-01-03       1   0     1        0        1           1   \n",
       "3        4  2011-01-04       1   0     1        0        2           1   \n",
       "4        5  2011-01-05       1   0     1        0        3           1   \n",
       "\n",
       "   weathersit      temp     atemp       hum  windspeed   cnt  \n",
       "0           2  0.344167  0.363625  0.805833   0.160446   985  \n",
       "1           2  0.363478  0.353739  0.696087   0.248539   801  \n",
       "2           1  0.196364  0.189405  0.437273   0.248309  1349  \n",
       "3           1  0.200000  0.212122  0.590435   0.160296  1562  \n",
       "4           1  0.226957  0.229270  0.436957   0.186900  1600  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 365 entries, 0 to 364\n",
      "Data columns (total 13 columns):\n",
      "instant       365 non-null int64\n",
      "season        365 non-null int64\n",
      "yr            365 non-null int64\n",
      "mnth          365 non-null int64\n",
      "holiday       365 non-null int64\n",
      "weekday       365 non-null int64\n",
      "workingday    365 non-null int64\n",
      "weathersit    365 non-null int64\n",
      "temp          365 non-null float64\n",
      "atemp         365 non-null float64\n",
      "hum           365 non-null float64\n",
      "windspeed     365 non-null float64\n",
      "cnt           365 non-null int64\n",
      "dtypes: float64(4), int64(9)\n",
      "memory usage: 39.9 KB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "instant       0\n",
       "season        0\n",
       "yr            0\n",
       "mnth          0\n",
       "holiday       0\n",
       "weekday       0\n",
       "workingday    0\n",
       "weathersit    0\n",
       "temp          0\n",
       "atemp         0\n",
       "hum           0\n",
       "windspeed     0\n",
       "cnt           0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 数据探索"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[     season_1  season_2  season_3  season_4\n",
      "0           1         0         0         0\n",
      "1           1         0         0         0\n",
      "2           1         0         0         0\n",
      "3           1         0         0         0\n",
      "4           1         0         0         0\n",
      "5           1         0         0         0\n",
      "6           1         0         0         0\n",
      "7           1         0         0         0\n",
      "8           1         0         0         0\n",
      "9           1         0         0         0\n",
      "10          1         0         0         0\n",
      "11          1         0         0         0\n",
      "12          1         0         0         0\n",
      "13          1         0         0         0\n",
      "14          1         0         0         0\n",
      "15          1         0         0         0\n",
      "16          1         0         0         0\n",
      "17          1         0         0         0\n",
      "18          1         0         0         0\n",
      "19          1         0         0         0\n",
      "20          1         0         0         0\n",
      "21          1         0         0         0\n",
      "22          1         0         0         0\n",
      "23          1         0         0         0\n",
      "24          1         0         0         0\n",
      "25          1         0         0         0\n",
      "26          1         0         0         0\n",
      "27          1         0         0         0\n",
      "28          1         0         0         0\n",
      "29          1         0         0         0\n",
      "..        ...       ...       ...       ...\n",
      "335         0         0         0         1\n",
      "336         0         0         0         1\n",
      "337         0         0         0         1\n",
      "338         0         0         0         1\n",
      "339         0         0         0         1\n",
      "340         0         0         0         1\n",
      "341         0         0         0         1\n",
      "342         0         0         0         1\n",
      "343         0         0         0         1\n",
      "344         0         0         0         1\n",
      "345         0         0         0         1\n",
      "346         0         0         0         1\n",
      "347         0         0         0         1\n",
      "348         0         0         0         1\n",
      "349         0         0         0         1\n",
      "350         0         0         0         1\n",
      "351         0         0         0         1\n",
      "352         0         0         0         1\n",
      "353         0         0         0         1\n",
      "354         1         0         0         0\n",
      "355         1         0         0         0\n",
      "356         1         0         0         0\n",
      "357         1         0         0         0\n",
      "358         1         0         0         0\n",
      "359         1         0         0         0\n",
      "360         1         0         0         0\n",
      "361         1         0         0         0\n",
      "362         1         0         0         0\n",
      "363         1         0         0         0\n",
      "364         1         0         0         0\n",
      "\n",
      "[365 rows x 4 columns],      mnth_1  mnth_2  mnth_3  mnth_4  mnth_5  mnth_6  mnth_7  mnth_8  mnth_9  \\\n",
      "0         1       0       0       0       0       0       0       0       0   \n",
      "1         1       0       0       0       0       0       0       0       0   \n",
      "2         1       0       0       0       0       0       0       0       0   \n",
      "3         1       0       0       0       0       0       0       0       0   \n",
      "4         1       0       0       0       0       0       0       0       0   \n",
      "5         1       0       0       0       0       0       0       0       0   \n",
      "6         1       0       0       0       0       0       0       0       0   \n",
      "7         1       0       0       0       0       0       0       0       0   \n",
      "8         1       0       0       0       0       0       0       0       0   \n",
      "9         1       0       0       0       0       0       0       0       0   \n",
      "10        1       0       0       0       0       0       0       0       0   \n",
      "11        1       0       0       0       0       0       0       0       0   \n",
      "12        1       0       0       0       0       0       0       0       0   \n",
      "13        1       0       0       0       0       0       0       0       0   \n",
      "14        1       0       0       0       0       0       0       0       0   \n",
      "15        1       0       0       0       0       0       0       0       0   \n",
      "16        1       0       0       0       0       0       0       0       0   \n",
      "17        1       0       0       0       0       0       0       0       0   \n",
      "18        1       0       0       0       0       0       0       0       0   \n",
      "19        1       0       0       0       0       0       0       0       0   \n",
      "20        1       0       0       0       0       0       0       0       0   \n",
      "21        1       0       0       0       0       0       0       0       0   \n",
      "22        1       0       0       0       0       0       0       0       0   \n",
      "23        1       0       0       0       0       0       0       0       0   \n",
      "24        1       0       0       0       0       0       0       0       0   \n",
      "25        1       0       0       0       0       0       0       0       0   \n",
      "26        1       0       0       0       0       0       0       0       0   \n",
      "27        1       0       0       0       0       0       0       0       0   \n",
      "28        1       0       0       0       0       0       0       0       0   \n",
      "29        1       0       0       0       0       0       0       0       0   \n",
      "..      ...     ...     ...     ...     ...     ...     ...     ...     ...   \n",
      "335       0       0       0       0       0       0       0       0       0   \n",
      "336       0       0       0       0       0       0       0       0       0   \n",
      "337       0       0       0       0       0       0       0       0       0   \n",
      "338       0       0       0       0       0       0       0       0       0   \n",
      "339       0       0       0       0       0       0       0       0       0   \n",
      "340       0       0       0       0       0       0       0       0       0   \n",
      "341       0       0       0       0       0       0       0       0       0   \n",
      "342       0       0       0       0       0       0       0       0       0   \n",
      "343       0       0       0       0       0       0       0       0       0   \n",
      "344       0       0       0       0       0       0       0       0       0   \n",
      "345       0       0       0       0       0       0       0       0       0   \n",
      "346       0       0       0       0       0       0       0       0       0   \n",
      "347       0       0       0       0       0       0       0       0       0   \n",
      "348       0       0       0       0       0       0       0       0       0   \n",
      "349       0       0       0       0       0       0       0       0       0   \n",
      "350       0       0       0       0       0       0       0       0       0   \n",
      "351       0       0       0       0       0       0       0       0       0   \n",
      "352       0       0       0       0       0       0       0       0       0   \n",
      "353       0       0       0       0       0       0       0       0       0   \n",
      "354       0       0       0       0       0       0       0       0       0   \n",
      "355       0       0       0       0       0       0       0       0       0   \n",
      "356       0       0       0       0       0       0       0       0       0   \n",
      "357       0       0       0       0       0       0       0       0       0   \n",
      "358       0       0       0       0       0       0       0       0       0   \n",
      "359       0       0       0       0       0       0       0       0       0   \n",
      "360       0       0       0       0       0       0       0       0       0   \n",
      "361       0       0       0       0       0       0       0       0       0   \n",
      "362       0       0       0       0       0       0       0       0       0   \n",
      "363       0       0       0       0       0       0       0       0       0   \n",
      "364       0       0       0       0       0       0       0       0       0   \n",
      "\n",
      "     mnth_10  mnth_11  mnth_12  \n",
      "0          0        0        0  \n",
      "1          0        0        0  \n",
      "2          0        0        0  \n",
      "3          0        0        0  \n",
      "4          0        0        0  \n",
      "5          0        0        0  \n",
      "6          0        0        0  \n",
      "7          0        0        0  \n",
      "8          0        0        0  \n",
      "9          0        0        0  \n",
      "10         0        0        0  \n",
      "11         0        0        0  \n",
      "12         0        0        0  \n",
      "13         0        0        0  \n",
      "14         0        0        0  \n",
      "15         0        0        0  \n",
      "16         0        0        0  \n",
      "17         0        0        0  \n",
      "18         0        0        0  \n",
      "19         0        0        0  \n",
      "20         0        0        0  \n",
      "21         0        0        0  \n",
      "22         0        0        0  \n",
      "23         0        0        0  \n",
      "24         0        0        0  \n",
      "25         0        0        0  \n",
      "26         0        0        0  \n",
      "27         0        0        0  \n",
      "28         0        0        0  \n",
      "29         0        0        0  \n",
      "..       ...      ...      ...  \n",
      "335        0        0        1  \n",
      "336        0        0        1  \n",
      "337        0        0        1  \n",
      "338        0        0        1  \n",
      "339        0        0        1  \n",
      "340        0        0        1  \n",
      "341        0        0        1  \n",
      "342        0        0        1  \n",
      "343        0        0        1  \n",
      "344        0        0        1  \n",
      "345        0        0        1  \n",
      "346        0        0        1  \n",
      "347        0        0        1  \n",
      "348        0        0        1  \n",
      "349        0        0        1  \n",
      "350        0        0        1  \n",
      "351        0        0        1  \n",
      "352        0        0        1  \n",
      "353        0        0        1  \n",
      "354        0        0        1  \n",
      "355        0        0        1  \n",
      "356        0        0        1  \n",
      "357        0        0        1  \n",
      "358        0        0        1  \n",
      "359        0        0        1  \n",
      "360        0        0        1  \n",
      "361        0        0        1  \n",
      "362        0        0        1  \n",
      "363        0        0        1  \n",
      "364        0        0        1  \n",
      "\n",
      "[365 rows x 12 columns],      weekday_0  weekday_1  weekday_2  weekday_3  weekday_4  weekday_5  \\\n",
      "0            0          0          0          0          0          0   \n",
      "1            1          0          0          0          0          0   \n",
      "2            0          1          0          0          0          0   \n",
      "3            0          0          1          0          0          0   \n",
      "4            0          0          0          1          0          0   \n",
      "5            0          0          0          0          1          0   \n",
      "6            0          0          0          0          0          1   \n",
      "7            0          0          0          0          0          0   \n",
      "8            1          0          0          0          0          0   \n",
      "9            0          1          0          0          0          0   \n",
      "10           0          0          1          0          0          0   \n",
      "11           0          0          0          1          0          0   \n",
      "12           0          0          0          0          1          0   \n",
      "13           0          0          0          0          0          1   \n",
      "14           0          0          0          0          0          0   \n",
      "15           1          0          0          0          0          0   \n",
      "16           0          1          0          0          0          0   \n",
      "17           0          0          1          0          0          0   \n",
      "18           0          0          0          1          0          0   \n",
      "19           0          0          0          0          1          0   \n",
      "20           0          0          0          0          0          1   \n",
      "21           0          0          0          0          0          0   \n",
      "22           1          0          0          0          0          0   \n",
      "23           0          1          0          0          0          0   \n",
      "24           0          0          1          0          0          0   \n",
      "25           0          0          0          1          0          0   \n",
      "26           0          0          0          0          1          0   \n",
      "27           0          0          0          0          0          1   \n",
      "28           0          0          0          0          0          0   \n",
      "29           1          0          0          0          0          0   \n",
      "..         ...        ...        ...        ...        ...        ...   \n",
      "335          0          0          0          0          0          1   \n",
      "336          0          0          0          0          0          0   \n",
      "337          1          0          0          0          0          0   \n",
      "338          0          1          0          0          0          0   \n",
      "339          0          0          1          0          0          0   \n",
      "340          0          0          0          1          0          0   \n",
      "341          0          0          0          0          1          0   \n",
      "342          0          0          0          0          0          1   \n",
      "343          0          0          0          0          0          0   \n",
      "344          1          0          0          0          0          0   \n",
      "345          0          1          0          0          0          0   \n",
      "346          0          0          1          0          0          0   \n",
      "347          0          0          0          1          0          0   \n",
      "348          0          0          0          0          1          0   \n",
      "349          0          0          0          0          0          1   \n",
      "350          0          0          0          0          0          0   \n",
      "351          1          0          0          0          0          0   \n",
      "352          0          1          0          0          0          0   \n",
      "353          0          0          1          0          0          0   \n",
      "354          0          0          0          1          0          0   \n",
      "355          0          0          0          0          1          0   \n",
      "356          0          0          0          0          0          1   \n",
      "357          0          0          0          0          0          0   \n",
      "358          1          0          0          0          0          0   \n",
      "359          0          1          0          0          0          0   \n",
      "360          0          0          1          0          0          0   \n",
      "361          0          0          0          1          0          0   \n",
      "362          0          0          0          0          1          0   \n",
      "363          0          0          0          0          0          1   \n",
      "364          0          0          0          0          0          0   \n",
      "\n",
      "     weekday_6  \n",
      "0            1  \n",
      "1            0  \n",
      "2            0  \n",
      "3            0  \n",
      "4            0  \n",
      "5            0  \n",
      "6            0  \n",
      "7            1  \n",
      "8            0  \n",
      "9            0  \n",
      "10           0  \n",
      "11           0  \n",
      "12           0  \n",
      "13           0  \n",
      "14           1  \n",
      "15           0  \n",
      "16           0  \n",
      "17           0  \n",
      "18           0  \n",
      "19           0  \n",
      "20           0  \n",
      "21           1  \n",
      "22           0  \n",
      "23           0  \n",
      "24           0  \n",
      "25           0  \n",
      "26           0  \n",
      "27           0  \n",
      "28           1  \n",
      "29           0  \n",
      "..         ...  \n",
      "335          0  \n",
      "336          1  \n",
      "337          0  \n",
      "338          0  \n",
      "339          0  \n",
      "340          0  \n",
      "341          0  \n",
      "342          0  \n",
      "343          1  \n",
      "344          0  \n",
      "345          0  \n",
      "346          0  \n",
      "347          0  \n",
      "348          0  \n",
      "349          0  \n",
      "350          1  \n",
      "351          0  \n",
      "352          0  \n",
      "353          0  \n",
      "354          0  \n",
      "355          0  \n",
      "356          0  \n",
      "357          1  \n",
      "358          0  \n",
      "359          0  \n",
      "360          0  \n",
      "361          0  \n",
      "362          0  \n",
      "363          0  \n",
      "364          1  \n",
      "\n",
      "[365 rows x 7 columns],      weathersit_1  weathersit_2  weathersit_3\n",
      "0               0             1             0\n",
      "1               0             1             0\n",
      "2               1             0             0\n",
      "3               1             0             0\n",
      "4               1             0             0\n",
      "5               1             0             0\n",
      "6               0             1             0\n",
      "7               0             1             0\n",
      "8               1             0             0\n",
      "9               1             0             0\n",
      "10              0             1             0\n",
      "11              1             0             0\n",
      "12              1             0             0\n",
      "13              1             0             0\n",
      "14              0             1             0\n",
      "15              1             0             0\n",
      "16              0             1             0\n",
      "17              0             1             0\n",
      "18              0             1             0\n",
      "19              0             1             0\n",
      "20              1             0             0\n",
      "21              1             0             0\n",
      "22              1             0             0\n",
      "23              1             0             0\n",
      "24              0             1             0\n",
      "25              0             0             1\n",
      "26              1             0             0\n",
      "27              0             1             0\n",
      "28              1             0             0\n",
      "29              1             0             0\n",
      "..            ...           ...           ...\n",
      "335             1             0             0\n",
      "336             1             0             0\n",
      "337             1             0             0\n",
      "338             0             1             0\n",
      "339             0             0             1\n",
      "340             0             0             1\n",
      "341             1             0             0\n",
      "342             1             0             0\n",
      "343             1             0             0\n",
      "344             1             0             0\n",
      "345             1             0             0\n",
      "346             1             0             0\n",
      "347             0             1             0\n",
      "348             0             1             0\n",
      "349             0             1             0\n",
      "350             0             1             0\n",
      "351             1             0             0\n",
      "352             1             0             0\n",
      "353             0             1             0\n",
      "354             0             1             0\n",
      "355             0             1             0\n",
      "356             1             0             0\n",
      "357             1             0             0\n",
      "358             1             0             0\n",
      "359             1             0             0\n",
      "360             0             1             0\n",
      "361             1             0             0\n",
      "362             1             0             0\n",
      "363             1             0             0\n",
      "364             1             0             0\n",
      "\n",
      "[365 rows x 3 columns]]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.preprocessing import OneHotEncoder\n",
    "\n",
    "categorical_features = ['season', 'mnth', 'weekday', 'weathersit']\n",
    "categorical = []\n",
    "#categorical_features = ['season']\n",
    "for feature in categorical_features:\n",
    "  categorical.append(pd.get_dummies(data[feature], prefix = feature))\n",
    "\n",
    "#ohe = OneHotEncoder()\n",
    "#ohe.fit_transform(data.season.values)\n",
    "#data.head()\n",
    "print categorical"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>instant</th>\n",
       "      <th>season</th>\n",
       "      <th>yr</th>\n",
       "      <th>mnth</th>\n",
       "      <th>holiday</th>\n",
       "      <th>weekday</th>\n",
       "      <th>workingday</th>\n",
       "      <th>weathersit</th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>hum</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>cnt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>365.000000</td>\n",
       "      <td>365.000000</td>\n",
       "      <td>365.0</td>\n",
       "      <td>365.000000</td>\n",
       "      <td>365.000000</td>\n",
       "      <td>365.000000</td>\n",
       "      <td>365.000000</td>\n",
       "      <td>365.000000</td>\n",
       "      <td>365.000000</td>\n",
       "      <td>365.000000</td>\n",
       "      <td>365.000000</td>\n",
       "      <td>365.000000</td>\n",
       "      <td>365.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>183.000000</td>\n",
       "      <td>2.498630</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.526027</td>\n",
       "      <td>0.027397</td>\n",
       "      <td>3.008219</td>\n",
       "      <td>0.684932</td>\n",
       "      <td>1.421918</td>\n",
       "      <td>0.486665</td>\n",
       "      <td>0.466835</td>\n",
       "      <td>0.643665</td>\n",
       "      <td>0.191403</td>\n",
       "      <td>3405.761644</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>105.510663</td>\n",
       "      <td>1.110946</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.452584</td>\n",
       "      <td>0.163462</td>\n",
       "      <td>2.006155</td>\n",
       "      <td>0.465181</td>\n",
       "      <td>0.571831</td>\n",
       "      <td>0.189596</td>\n",
       "      <td>0.168836</td>\n",
       "      <td>0.148744</td>\n",
       "      <td>0.076890</td>\n",
       "      <td>1378.753666</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.059130</td>\n",
       "      <td>0.079070</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.022392</td>\n",
       "      <td>431.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>92.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.325000</td>\n",
       "      <td>0.321954</td>\n",
       "      <td>0.538333</td>\n",
       "      <td>0.135583</td>\n",
       "      <td>2132.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>183.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.479167</td>\n",
       "      <td>0.472846</td>\n",
       "      <td>0.647500</td>\n",
       "      <td>0.186900</td>\n",
       "      <td>3740.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>274.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.656667</td>\n",
       "      <td>0.612379</td>\n",
       "      <td>0.742083</td>\n",
       "      <td>0.235075</td>\n",
       "      <td>4586.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>365.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.849167</td>\n",
       "      <td>0.840896</td>\n",
       "      <td>0.972500</td>\n",
       "      <td>0.507463</td>\n",
       "      <td>6043.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          instant      season     yr        mnth     holiday     weekday  \\\n",
       "count  365.000000  365.000000  365.0  365.000000  365.000000  365.000000   \n",
       "mean   183.000000    2.498630    0.0    6.526027    0.027397    3.008219   \n",
       "std    105.510663    1.110946    0.0    3.452584    0.163462    2.006155   \n",
       "min      1.000000    1.000000    0.0    1.000000    0.000000    0.000000   \n",
       "25%     92.000000    2.000000    0.0    4.000000    0.000000    1.000000   \n",
       "50%    183.000000    3.000000    0.0    7.000000    0.000000    3.000000   \n",
       "75%    274.000000    3.000000    0.0   10.000000    0.000000    5.000000   \n",
       "max    365.000000    4.000000    0.0   12.000000    1.000000    6.000000   \n",
       "\n",
       "       workingday  weathersit        temp       atemp         hum   windspeed  \\\n",
       "count  365.000000  365.000000  365.000000  365.000000  365.000000  365.000000   \n",
       "mean     0.684932    1.421918    0.486665    0.466835    0.643665    0.191403   \n",
       "std      0.465181    0.571831    0.189596    0.168836    0.148744    0.076890   \n",
       "min      0.000000    1.000000    0.059130    0.079070    0.000000    0.022392   \n",
       "25%      0.000000    1.000000    0.325000    0.321954    0.538333    0.135583   \n",
       "50%      1.000000    1.000000    0.479167    0.472846    0.647500    0.186900   \n",
       "75%      1.000000    2.000000    0.656667    0.612379    0.742083    0.235075   \n",
       "max      1.000000    3.000000    0.849167    0.840896    0.972500    0.507463   \n",
       "\n",
       "               cnt  \n",
       "count   365.000000  \n",
       "mean   3405.761644  \n",
       "std    1378.753666  \n",
       "min     431.000000  \n",
       "25%    2132.000000  \n",
       "50%    3740.000000  \n",
       "75%    4586.000000  \n",
       "max    6043.000000  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd71dd02610>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 查看cnt的分布，看是否有可以剔除的离散点\n",
    "fig = plt.figure()\n",
    "sns.distplot(data.cnt.values, bins=30, kde=True)\n",
    "plt.xlabel('cnt of capital bikeshare', fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd71dc1f690>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(range(data.shape[0]), data[\"cnt\"].values,color='purple')\n",
    "plt.title(\"Distribution of cnt\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd74805e090>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(data.season);\n",
    "plt.xlabel('season of capital bikeshare');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd71dc90610>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(data.holiday);\n",
    "plt.xlabel('holiday of capital bikeshare'); # 绝大部分人节假日肯定会骑单车游玩"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd71dc7e590>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(data.workingday);\n",
    "plt.xlabel('workingday of capital bikeshare'); \n",
    "# 相对来说工作日骑单车人数比节假日要多"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd71baa7150>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(data.weathersit);\n",
    "plt.xlabel('weathersit of capital bikeshare'); # 天气越好骑行人数越多"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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0h34Qw6m5twD/PPJ4xdCl/TYMfzy+yXCKZ65ajmQY4303YAb4LPCydt/G9lg7zrPtBuAahhHvdgJ2BfZr9x0I3KM9n3syDJ1wyKzHPYVhDPd7AD/cekwYupi/bb4agDu3Y3STVvOZwHHzHd9ZNZ/Yar5/2/61wKdnHbs7j6x7VHte/w4cNbLeccBp7RivYxjB8eh239HAG9ox2Qn4XX59jeAL2mPdtm37deBp7b5dgUcDN2+P+W7g/SP73AJcCOzbXvedgPcD/9SO427tsZ+61u9xb3O899a6AG+r9MJuG+gvAN46a52PAoe16S20cSza/KuBD4/MPxw4d2S+gING5p8BnD5PLd8BDh6Zfyjtqi9zhemsbf8KeN+Yz/k44NhZj3u3kftfAZzQpo9gO4E+x2MfAnxpvuM7a90TgXeMzN8S+AXD2O1zBfqbgfOA549sE4bxhu40suy+wPfa9JHAqVsfZ47X/omznvcb5ql1P+DHI/NbgCNH5ncHfg7cbGTZocAZa/0e97btzQ9FbzhuDzw2ycNHlu3E0Mre6rKR6f+eY372h3mjFwb4PkOLcC63bfePs+5sezH8QdhGkv2BYxiG0b0xQ2v43QvUeI9xdppkN+B1DC3fdQz/Bfx4zJr/336r6tokVzI854vmWPdhwLUMLe6tZhha0eckvxqyOwyXTINhvPwjgI+1+4+vqmNGtv/Pkemftn2T5OYM48gfBOzS7l+XZIf69fjjozXenuF9culIHTea53lojXkOvV+zR127iKGFfuuR2y1mhcBijY4jvYH5x7e+hCEYxll3tosYTgPN5WSGUxJ7VdWtGAJx9gULxqlxrhHqjm7L71lVOwNPnOOxt+dX+01yS4ZTH/M95zcCHwE+lOQWbdkVDH9E9x15vW5V7RsyVXVNVR1eVXdk+O/peUkeOEZdhzMMUbt/e17331rmyDqjx+Mihhb6+pE6dq6qfcfYlybMQO/XZQwXvt3qbcDDkzw0yQ5JbprkwNEPxJbg+e1Dtr2A5wLvnGe9U4CXJJlJsh74m1bPON4OPCjDlep3bB/U7tfuWwdcWVU/S3If4PFzbP/XSW6eZF/gT+ap8YcM15gdPV7rGFrNVyXZE3j+mPVudXD7MPfGDNc9/XxVba9V+yyGD4g/mORmVfVLhqA/tv23QJI9kzy0Tf9BkjtnaDZfzXBKZ5wr/Kxj+ENxVZLbAC/d3spVdSnwMeDVSXZOcqP2gfHvjbEvTZiB3q+jGUL0qiR/0cLkkcCLGALsIoaQWs574FTgHIYxov8VOGGe9Y4Czga+wjAG9xfbsgVV1YUMH+QeDlzZ9nWvdvczgCOTXMPwR+JdczzEpxg+7D0deFVVfWyOffwUeDnwmXa8DgD+FvhN4Cftub13nHpHnMwQllcyXK3oCdtbuaoK2MzwupzavpXyglb7WUmuZhgz/65tk73b/LXA54B/qKotY9R1HHAzhv8AzmL4z2Ahf8xwSutrDKed3gPsMcZ2mjDHQ9eSJCmGKxl9e61rmUsm3FlImga20CWpEwa6JHXCUy6S1Alb6JLUCQNdkjphoEtSJwx0SeqEgS5JnTDQJakT/wfIBqz7aoIwxwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd71baa72d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "sns.distplot(data.temp.values, bins = 30, kde = False)\n",
    "plt.xlabel('temp of capital bikeshare', fontsize = 12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd71ba346d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "sns.distplot(data.windspeed.values, bins = 30, kde = False)\n",
    "plt.xlabel('windspeed of capital bikeshare', fontsize = 12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 风速达到高点这，突然有一部分骑行，先剔除这几个离群点\n",
    "data = data[data.windspeed < 0.5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd71b972dd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "sns.distplot(data.hum.values, bins = 30, kde = False)\n",
    "plt.xlabel('hum of capital bikeshare', fontsize = 12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(13, 13)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cols=data.columns\n",
    "data_corr = data.corr().abs()\n",
    "data_corr.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd71b8ce450>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplots(figsize=(13, 9))\n",
    "sns.heatmap(data_corr,annot=True)\n",
    "sns.heatmap(data_corr, mask=data_corr < 1, cbar=False)\n",
    "#plt.savefig('capital_bikeshare.png' )\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "instant and mnth = 1.00\n",
      "temp and atemp = 1.00\n",
      "season and mnth = 0.83\n",
      "instant and season = 0.83\n",
      "atemp and cnt = 0.78\n",
      "temp and cnt = 0.77\n",
      "weathersit and hum = 0.58\n",
      "season and cnt = 0.54\n"
     ]
    }
   ],
   "source": [
    "#Set the threshold to select only highly correlated attributes\n",
    "threshold = 0.5\n",
    "# List of pairs along with correlation above threshold\n",
    "corr_list = []\n",
    "#size = data.shape[1]\n",
    "size = data_corr.shape[0]\n",
    "\n",
    "#Search for the highly correlated pairs\n",
    "for i in range(0, size): #for 'size' features\n",
    "    for j in range(i+1,size): #avoid repetition\n",
    "        if (data_corr.iloc[i,j] >= threshold and data_corr.iloc[i,j] < 1) or (data_corr.iloc[i,j] < 0 and data_corr.iloc[i,j] <= -threshold):\n",
    "            corr_list.append([data_corr.iloc[i,j],i,j]) #store correlation and columns index\n",
    "\n",
    "#Sort to show higher ones first            \n",
    "s_corr_list = sorted(corr_list,key=lambda x: -abs(x[0]))\n",
    "\n",
    "#Print correlations and column names\n",
    "for v,i,j in s_corr_list:\n",
    "    print (\"%s and %s = %.2f\" % (cols[i],cols[j],v))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Cyj41VWrLBMVondZSh2KkTq0Wt9awtI9UDaF7d+oBI+KLgW8C/gVASmkZWN6p+5duRVkmri+2aZdbf/KgUa+REqz0qetXu9guOXXuMjNzCxybnuKh40e58/CoQaQDq1IIpZT+bAcf80XAZ4Gfj4ivBj4EvD6l9IUdfAypkla7wzOtdt+6yaLGtQp1/WpPn7/ChdnudYAvzM7zwNlLnDnRZHy06t+D0v6S45l/CHgZ8G9TSo9FxE/R/cK8H1xbFBEngZMAR44cGXiTOhiKkTp3T/XfGxwBE2PVrli1Ve3M3MJN08VIvdL9SvtRjhD6NPDplNJjvelHee63tgKQUnoYeBig2WxW3BMv3ZrWcoer15f61k0WjVsYCW1ee2x66sZIaHW6tdxxJKQDa+DP/JTS/4uIP4+Ir0wpfQJ4BfCxQfchARSNOncUjb7HhCK64VLlmNBWtQ/ed89Nx4SKhiMhHVyR0uAHGRFxFPhZYITu10R8Z0rpmc3qm81munjx4qDa0wHj2XHSrqj0xM6yDyCldAlo5nhsab1aLShGBv9SGB9zF5xU9cOqkiTtOENIkpSNISRJysYQkiRlYwhJkrIxhCRJ2RhCkqRsDCFJUjaGkCQpG0NIkpSNISRJysYQkiRlYwhJkrIxhCRJ2RhCkqRsDCFJUjaGkCQpG0NIkpSNISRJysYQkiRlYwhJkrIxhCRJ2RhCkqRsDuVuQFqvLBOLKx1INy+rBZQbzN9u3Y3HTFCM1mktdShG6tRqUX1lSdtmCGmolGXi+mKbdlnetKxRr5ESrGywbDt1qxbbJafOXWZmboFj01M8dPwodx4eNYikATCENFRa7Q7PtNobLpssalzbZNl26ladPn+FC7PzAFyYneeBs5c4c6LJ+KgvD2m3+SrTUClG6tw9VWy4LAImxhp976Nq3aqZuYWbpouReuX1JW2fIaSh0lrucPX60obLJotGxZFQtbpVx6anboyEVqdbyx1HQtIA+CrTUCkade4oGhseE4roBky/Yz1V61Y9eN89Nx0TKhqOhKRBMIQ0VGq1YGKs0efsuP6fLKhaB1A04Mz9Tc+OkzIwhDR0arWgGMnz1Bwf8yUhDZIfVpUkZWMISZKyMYQkSdkYQpKkbAwhSVI2hpAkKRtDSJKUjSEkScrGEJIkZWMISZKyMYQkSdkYQpKkbAwhSVI2hpAkKRtDSJKUjSEkScrGEJIkZWMISZKyMYQkSdkYQpKkbAwhSVI2hpAkKZtsIRQR9Yj4cES8O1cPkqS8DmV87NcDTwBfnLEHVVSWicWVDjWgTNXWqUW12vV1ZYJitE5rqUMxUqdWi231LGn4ZQmhiPgy4B8BPwL8uxw9qLqyTFxfbENASrBSln3XadRrlWrX1y22S06du8zM3ALHpqd46PhR7jw8ahBJ+1SukdBPAt8HTGR6fN2CVrvDM602k0WDa612pXUmi1ql2vV1p89f4cLsPAAXZud54OwlzpxoMj6ac9AuabcM/JUdEa8Cnk4pfSgiXr5F3UngJMCRI0cG1J02UozUuXuqIAImxhqV1qlau75uZm7hOctn5hYoRuq31rCkPSPHn5ffAHxbRLwSGAO+OCLemVL6jrVFKaWHgYcBms1mxaMQ2g2t5Q5Xry/d4kioWu36umPTUzdGQqvTreWOIyFpn4qU8r2/90ZCb0wpvWqrumazmS5evDiYpnQTjwlJ2oZKL1r/vFRftVowMdZYc3ZctTP7u2e99a9dW1c04Mz9Tc+Okw6IrCGUUno/8P6cPaiaWi0oRgb7dBkf828kab/zigmSpGwMIUlSNoaQJCkbQ0iSlI0hJEnKxhCSJGVjCEmSsjGEJEnZGEKSpGwMIUlSNoaQJCkbQ0iSlI0hJEnKxhCSJGVjCEmSsjGEJEnZGEKSpGwMIUlSNoaQJCkbQ0iSlI0hJEnKxhCSJGVjCEmSsjmUuwHdurJMLK50IG1dVwso+9RUrS0TFKN1WksdipE6tVpUb1iSNmEI7TFlmbi+2KZdllvWNeo1UoKVPnVVahfbJafOXWZmboFj01M8dPwodx4eNYgk3TZDaI9ptTs802r3rZssalyrUFel9vT5K1yYnQfgwuw8D5y9xJkTTcZHffpIuj2+i+wxxUidu6eKvnURMDHWqHSf/Wpn5hZumi5G6pXuW5K2YgjtMa3lDlevL/WtmywatzAS2rr22PTUjZHQ6nRrueNISNJt811kjykade4oGn2PCUV0w6XKMaF+tQ/ed89Nx4SKhiMhSbfPENpjarVgYqxxC2fHVTsLf6vaogFn7m96dpykHWcI7UG1WlCM5PnVjY/5lJG0c/ywqiQpG0NIkpSNISRJysYQkiRlYwhJkrIxhCRJ2RhCkqRsDCFJUjaGkCQpG0NIkpSNISRJysYQkiRlYwhJkrIxhCRJ2RhCkqRsDCFJUjaGkCQpG0NIkpSNISRJysYQkiRlYwhJkrIxhCRJ2Qw8hCLi7oj4vYh4IiI+GhGvH3QPkqThcCjDY64A/z6l9McRMQF8KCLel1L6WIZedlxZJhZXOpC607WAMvVfb7WuTPBFIzWW2uXWj5OgGK3TWupQjNSp1WIHupekwRp4CKWUngKe6t2+HhFPAC8A9nwIlWXi+mKbdtkNkEa9RkqwUm4dKKt1zy6tcP5Dn+b41x4hthijLrZLTp27zMzcAsemp3jo+FHuPDxqEEnac7IeE4qIaeBrgMdy9rFTWu0Oz7TaPLvY4dnFDinBtTXTm/2s1p06d5lveeldfGF56/pT5y5zYXaelTJxYXaeB85eotXu5P7vS9Ity7E7DoCIGAd+FXhDSunzGyw/CZwEOHLkyIC7255ipM7dU8WN6QiYGGv0XW+1bmZugb/1JeN962fmFm6aLkbqt96wJGWWJYQiokE3gH4xpXR+o5qU0sPAwwDNZrPCUZX8Wssdrl5fujE9WTS41mr3XW+17tj0FE8+/Syjh7YeoB6bnuLC7PxzplvLHcZHs/1NIUnbEikN9v09IgJ4O7CQUnpDlXWazWa6ePHi7ja2AzwmJEk3VHpDyhFC3wj8IXAFWH13/v6U0m9tts5eCSHw7DhJ6qn0ppTj7Lj/TcXm9qJaLShGbn+zFqPVzhkZH3MXnKS9yysmSJKyMYQkSdkYQpKkbAwhSVI2hpAkKRtDSJKUjSEkScrGEJIkZWMISZKyMYQkSdkYQpKkbAwhSVI2hpAkKRtDSJKUjSEkScrGEJIkZWMISZKyMYQkSdkYQpKkbAwhSVI2hpAkKRtDSJKUzaHcDey2TqdkaaWsVFsLKNPt1ZUJitE6raUOxUidWi1uoVtJOlj2dQh1OiXXl1ZYKfuHUKNeIyX61m5Vt9guOXXuMjNzCxybnuKh40e58/CoQSRJm9jXIdRqd7jWaleqnSxqlWq3qjt9/goXZucBuDA7zwNnL3HmRJPx0X29mSVp2/b1u+Ph0UMUI9X+ixEwMda4rbqZuYWbpouReqXHl6SDaF+H0BeWVph/drlS7WTRqDgS2rzu2PTUjZHQ6nRrueNISJI2sa/fHYtGnU7RqHRMKKIbMP1qt6p78L57bjomVDQcCUnSZvZ1CNXrNSZGD93i2XH9z1rfrK5owJn7m54dJ0kV7esQgm4QFfXBfxxqfGzfb1pJum1+WFWSlI0hJEnKxhCSJGVjCEmSsjGEJEnZGEKSpGwMIUlSNoaQJCkbQ0iSlI0hJEnKxhCSJGVjCEmSsjGEJEnZREopdw99RcRngT+7jbt4HnB1h9oZlL3YM9j3IO3FnsG+Bylnz1dTSvf2K9oTIXS7IuJiSqmZu49bsRd7BvsepL3YM9j3IO2Fnt0dJ0nKxhCSJGVzUELo4dwNbMNe7Bnse5D2Ys9g34M09D0fiGNCkqThdFBGQpKkIbSvQygi7o2IT0TEkxHx5tz9bCUi5iLiSkRcioiLvXlTEfG+iPjT3r93DEGfb4uIpyPi8TXzNuwzuh7qbf/LEfGyIer5hyLiL3rb+1JEvHLNstO9nj8REd+So+deH3dHxO9FxBMR8dGIeH1v/tBu7y16HurtHRFjEfHBiPhIr+8f7s1/YUQ81tvWvxwRI735o73pJ3vLp4es70ci4pNrtvfR3vzsz5GbpJT25Q9QB/4v8CJgBPgI8JLcfW3R7xzwvHXz/gvw5t7tNwNvHYI+vwl4GfB4vz6BVwK/DQTwdcBjQ9TzDwFv3KD2Jb3nyijwwt5zqJ6p77uAl/VuTwB/0utvaLf3Fj0P9fbubbPx3u0G8FhvG/4K8Lre/J8Bvqt3+7uBn+ndfh3wy5meI5v1/Qjw2g3qsz9H1v/s55HQ1wJPppRmU0rLwC8Br87c0616NfD23u23A6/J2AsAKaU/ABbWzd6sz1cD70hdHwAmI+KuwXT61zbpeTOvBn4ppbSUUvok8CTd59LApZSeSin9ce/2deAJ4AUM8fbeoufNDMX27m2zZ3uTjd5PAv4+8Ghv/vptvfo7eBR4RUTEgNq9YYu+N5P9ObLefg6hFwB/vmb602z9YsgtAe+NiA9FxMnevOenlJ6C7osb+JJs3W1tsz6H/XfwPb1dEm9bs6tzKHvu7e75Grp/6e6J7b2uZxjy7R0R9Yi4BDwNvI/uqOxaSmllg95u9N1b/jngzsF23LW+75TS6vb+kd72/omIGO3NG5rtvWo/h9BGf5UM86mA35BSehnwrcC/iYhvyt3QDhjm38H/AL4cOAo8BfzX3vyh6zkixoFfBd6QUvr8VqUbzMvS+wY9D/32Til1UkpHgS+jOxr72xuV9f4d2r4j4qXAaeDFwDFgCnhTr3xo+l61n0Po08Dda6a/DPjLTL30lVL6y96/TwO/RvdF8JnVoXLv36fzdbilzfoc2t9BSukzvRdvCZzhr3cBDVXPEdGg+2b+iyml873ZQ729N+p5r2xvgJTSNeD9dI+ZTEbEod6itb3d6Lu3/G9QfZfvrljT97293aIppbQE/DxDvL33cwjNAF/RO7tlhO7Bw3dl7mlDEXE4IiZWbwP/EHicbr8nemUngN/I02Ffm/X5LuD+3hk5Xwd8bnU3Um7r9oP/Y7rbG7o9v6539tMLga8APjjo/qB7JhPwc8ATKaUfX7NoaLf3Zj0P+/aOiL8ZEZO9218EfDPd41m/B7y2V7Z+W6/+Dl4L/G7qHfkfpE36/viaP1KC7nGstdt7uF6Tuc+M2M0fumeC/Andfbs/kLufLfp8Ed0zhD4CfHS1V7r7mH8H+NPev1ND0OtZurtT2nT/qvpXm/VJd+j/33vb/wrQHKKef6HX02W6L8y71tT/QK/nTwDfmnFbfyPdXSWXgUu9n1cO8/beoueh3t7APcCHe/09DvyH3vwX0Q3FJ4FzwGhv/lhv+sne8hcNWd+/29vejwPv5K/PoMv+HFn/4xUTJEnZ7OfdcZKkIWcISZKyMYQkSdkYQpKkbAwhSVI2hpC0TRHxR9tc7zUR8ZLbeNzpiPhn211fGiaGkLRNKaWv3+aqr6F79ejtmgYMIe0Lfk5I2qaIeDalNB4RL6f7VQVXgZcCHwK+I6WUIuJHgW8DVoD3AueBd9O94OXngH9K90rNJ+l+5ciTwD9PKbUi4hHg80AT+FLg+1JKj0bEB+he1+yTwNtTSj8xmP+xtPMO9S+RVMHXAH+H7nW4/g/wDRHxMbqXqHlxL5AmU0rXIuJdwLtTSo8CRMS1lNKZ3u3/RPeKDv+td7930b0KwYvpXmngUbrfIfTGlNKrBvffk3aHu+OknfHBlNKnU/cCnZfo7jL7PLAI/GxE/BOgtcm6L42IP4yIK8C30w2zVb+eUipTSh8Dnr977Ut5GELSzlhac7sDHErd75n5WrpXlH4N8J5N1n0E+J6U0lcBP0z3umQb3e/AvzRN2m3ujpN2Se87dYqU0m/1juM82Vt0ne5XX6+aAJ7qfQXCtwN/0eeu168v7VmOhKTdMwG8OyIuA78PfG9v/i8BpyLiwxHx5cAP0v320fcBH69wv5eBlYj4SER8b99qaYh5dpwkKRtHQpKkbAwhSVI2hpAkKRtDSJKUjSEkScrGEJIkZWMISZKyMYQkSdn8fyescR9ASuGBAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd71b874310>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd71dd94490>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd7151ccf10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd7150e19d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd7150a82d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd715078e10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd71517c310>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd71b864e50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for v, i, j in s_corr_list:\n",
    "    sns.pairplot(data, size = 6, x_vars = cols[i], y_vars = cols[j] )\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.14"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
